A new prediction method of industrial atmospheric pollutant emission intensity based on pollutant emission standard quantification

Abstract

Industrial emissions are the main source of atmospheric pollutants in China. Accurate and reasonable prediction of the emission of atmospheric pollutants from single enterprise can determine the exact source of atmospheric pollutants and control atmospheric pollution precisely. Based on China’s coking enterprises in 2020, we proposed a quantitative method for pollutant emission standards and introduced the quantification results of pollutant emission standards (QRPES) into the construction of support vector regression (SVR) and random forest regression (RFR) prediction methods for SO 2 emission of coking enterprises in China. The results show that, affected by the types of coke ovens and regions, China’s current coking enterprises have implemented a total of 21 emission standards, with marked differences. After adding QRPES, it was found that the root mean squared error (RMSE) of SVR and RFR decreased from 0.055 kt/a and 0.059 kt/a to 0.045 kt/a and 0.039 kt/a, and the R 2 increased from 0.890 and 0.881 to 0.926 and 0.945, respectively. This shows that the QRPES can greatly improve the prediction accuracy, and the SO 2 emissions of each enterprise are highly correlated with the strictness of standards. The predicted result shows that 45% of SO 2 emissions from Chinese coking enterprises are concentrated in Shanxi, Shaanxi and Hebei provinces in central China. The method created in this paper fills in the blank of forecasting method of air pollutant emission intensity of single enterprise and is of great help to the accurate control of air pollutants. Electronic Supplementary Material Supplementary material is available in the online version of this article at 10.1007/s11783-023-1608-1 and is accessible for authorized users.

Elucidation of spatial disparities of factors that affect air pollutant concentrations in industrial regions at a continental level

“…The relationship between the air pollutant concentrations and the characteristics of industrial regions can be determined using traditional statistical methods (Yang et al, 2019). Furthermore, advanced machinelearning algorithms, including support vector machines and random forests, were developed to measure industrial air-pollutant concentrations (Ju et al, 2023). However, the current knowledge about the spatial patterns that indicate the relationship between the anthropological or environmental features and the air pollutant concentrations in industrial regions is limited.…”

İklimsel Değişkenlerin Partikül Madde ve Kükürt Dioksit Üzerindeki Etkisi

“…Particulate matter less than 10 micrometres in diameter are called PM 10 . Some researchers have examined the characteristics, dispersion, and modelling of air pollutants, PM 10 and SO 2 , in recent years [19][20][21][22][23][24][25]. Climatological parameters are in interact both with air pollutants and with each other in the atmospheric environment [26][27][28][29][30].…”